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Hi, welcome to my webpage!
Currently, I am working as an Assistant Professor in the Department of Computer Science and Engineering at IIT Hyderabad.
Prior to joining IITH, I spent three pleasant years in the Shivalik Range of the Himalayas as an Assistant Professor at the School of Computing and Electrical Engineering (SCEE), IIT Mandi.
I am an algorithmist, and work in the intersection of theory and practice. I am interested in developing "extremely simple", practical approximation algorithms with provable performance guarantees for real-world problems. Among other things, my current research focuses around sketching/dimensionality reduction algorithms. In particular, I'm focusing on: a) developing new sketching/dimensionality reduction algorithms for various data types, in particular, tensors, and similarity measures, b) improving existing sketching/dimensionality reduction algorithms by making them fast, scalable and accurate, and c) exploring applicability of such results in various machine learning tasks such as learning node embedding in large scale network, itemset mining, model compression etc.
I generally use techniques from matrix algebra, sampling, random projection, and randomized hashing.
I earned my Ph.D. in Theoretical Computer Science from Chennai Mathematical Institute.
Previously, I have been working with industry research labs in the areas of algorithms in Data Science and Machine Learning at Wipro-AI Research, Bangalore; and TCS Innovation Labs, New Delhi. I have also spent a few months at IIIT Bangalore as a Research Associate.
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Ph.D. Hiring: I am looking for highly motivated PhD students to work in the areas of “developing scalable similarity search algorithms for tensors”(under the board areas of algorithms for massive datasets). The candidates should have a solid foundation in algorithm design and analysis, linear algebra, and probability theory.
For eligibility criteria and other details, please refer to https://cse.iith.ac.in/admissions/phd.html
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Note: I am not hiring for any internship/short-term project position. Unfortunately, I am not able to individually respond to such queries. If you are interested, please apply through the IITH SURE internship program as and when they are put up on the institute’s website.
My Erdös Number is 3.
Recent News:
Our paper "Sparsifying Count Sketch" joint work with Bhisham Dev Verma, and Punit Pankaj Dubey, has been accepted to the Information Process Letters, 2024.
Our paper “Unbiased Estimation of Inner Product via Higher Order Count Sketch” joint work with Bhisham Dev Verma and Manoj Thakur, has been accepted to the Information Process Letters, 2024.
Our paper “One-pass additive-error subset selection for $\ell_{p}$ subspace approximation and $(k,p)$-clustering” joint work with Amit Deshpande, has been accepted to the Algorithmica journal, 2023. (paper link)
Old News:
Intership Hiring: If you have a strong foundation in linear algebra, probability, algorithms, and have a deep interest in working on some foundational theoretical problems in the areas mentioned above during summer break (May-July, 2023), please drop me an email.
IITH is offering summer internship under Summer Undergraduate Research Exposure (SURE) scheme. The link for the applications is as follows: https://iith.ac.in/research/SURE/
Applications may be submitted by 22th Feb. 2023.
My first research student graduated!! Punit successfully defended his MS by Research thesis on 14th October 2022. In his thesis, he worked on improving sketching algorithms via sparsification.
Our paper “Improving Sign-Random-Projection via Count Sketch” joint work with Punit Pankaj Dubey, Bhisham Dev Verma, and Keegan Kang was accepted to the UAI-2022.
Our paper “One-pass additive-error subset selection for $\ell_{p}$ subspace approximation” joint work with Amit Deshpande was accepted to the ICALP-2022.
Our paper “Variance reduction in Feature Hashing using MLE and Control Variate Method” joint work with Bhisham Dev Verma and Manoj Thakur accepted to the Machine Learning journal, 2022.
Our paper “Efficient Binary Embedding of Categorical Data using BinSketch” joint work with Bhisham Dev Verma, and Debajyoti Bera has been accepted to the journal of Data Mining and Knowledge Discovery-2022.
Our paper “Dimensionality Reduction for Categorical Data” joint work with Debajyoti Bera and Bhisham Dev Verma got accepted to the IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.
Our paper “QUINT: Node embedding using network hashing” joint work with Debajyoti Bera, Bhisham Dev Verma, Biswadeep Sen, and Tanmoy Chakraborty got accepted to the IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.
Our paper "Feature Hashing with Insertion and Deletion of Features" joint work with Hrushikesh Sudam Sarode, Suryakant Bhardwaj and Raghav Kulkarni accepted to the IEEE International Conference on Big Data (IEEE-Big data-2021).
Our paper “Improving Hashing Algorithms for Similarity Search via MLE and the Control Variates Trick” joint work with Keegan Kang, Sergey Kushnarev, Weipin Wong, Haikal Yeo, Yijia Chen accepted to the 13th Asian Conference on Machine Learning (ACML 2021).
Delivered a keynote talk at “Data Analytics and Predictive technologies” organised by IIT-BHU on 8th July, 2021. I presented our recent work on "Sketching and Sampling Techniques for Big Data ''.
Our paper “Variance Reduction in Frequency Estimators via Control Variates Method” joint work with Raghav Kulkarni, has been accepted to the 37th Conference on Uncertainty in Artificial Intelligence (UAI, 2021) -- a core A* ranked conference in Computer Science.
Our paper "Improving Tug-of-War sketch using Control-Variates method" joint work with Bhisham Dev Verma and Raghav Kulkarni has been accepted to SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21).
Delivered a keynote talk at “Online short term course on Data Analytics and its Application in Industries” organised by IIT-BHU on 19th December, 2020. I presented our recent paper “Efficient Sketching Algorithms for Sparse Binary Data”.
Our following two papers accepted to ACML 2020
1) Randomness Efficient Feature Hashing for Sparse Binary Data. Joint work with Karthik Revanuru, Anirudh Ravi, Raghav Kulkarni.
2) Scaling up Simhash. Joint work with Anup Deshmukh, Pratheeksha Nair, Anirudh Ravi. This paper was also invited for Special Issue in Springer Nature Computer Science.
Our paper "Subspace approximation with outliers", joint work with Amit Deshpande, has been accepted to The 26th International Computing and Combinatorics Conference - COCOON 2020 -- a core A ranked conference in Computer Science. We won the Best Paper Award at COCOON 2020 for this submission.
Our paper "Robust k-means++", joint work with Amit Deshpande, Praneeth Kacham, has been accepted to Association for Uncertainty in Artificial Intelligence - UAI 2020 -- a core A* ranked conference in Computer Science.
I am serving as a Programme Committee member of "The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases" (ECML-PKDD) 2020.
We (Mukesh Prasad, Rameshwar Pratap, Rajiv Ratn Shah, Weiping Ding, Javier Andreu-Perez, Guandong Xu) are organising a special session on “Feature Extraction and Learning on Image and Text Data” to be held in conjunction with the conference “IEEE International Conference on Systems, Man and Cybernetics (SMC)-2020”. You may consider submitting your paper. Further details are available here.
I am serving as a Programme Committee member of "The Sixth IEEE International Conference on Multimedia Big Data" (IEEE-BigMM) 2020.
Amit Sangroya (TCS Research) and I are co-chairing IEEE BigMM’20 Grand Challenge which is to be held in conjunction with the IEEE International Conference on Multimedia Big Data from September 24-26, 2020 at New Delhi. You may consider submitting your Grand challenge proposal to us. Further details are available here.